Deep Reinforcement Learning-Based Access Control for Buffer-Aided Relaying Systems With Energy Harvesting

Zhang, Haodi; Zhan, Da; Zhang, Chen Jason; Wu, Kaishun; Liu, Ye; Luo, Sheng

doi:10.1109/access.2020.3014791

Cited by 8 publications

(5 citation statements)

References 30 publications

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“…In [26], Sun et al proposed a dynamic resource reservation and DRLbased autonomous virtual resource slicing framework for the next generation radio access network. Additionally, regarding the wireless access control problems, in [27]- [29], the DRL based routing selection approaches have been proposed, and in [30], [31], the DRL based dynamic power allocation approaches have been investigated. In our previous work [32], we also proposed a DRL-based radio access control mechanism for disaster response network, but without the consideration of UE's mobility and buffering capacity.…”

Section: Related Workmentioning

confidence: 99%

Wireless Access Control in Edge-Aided Disaster Response: A Deep Reinforcement Learning-Based Approach

et al. 2021

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The communication infrastructure is most likely to be damaged after a major disaster occurred, which would lead to further chaos in the disaster stricken area. Modern rescue activities heavily rely on the wireless communications, such as safety status report, disrupted area monitoring, evacuation instruction, rescue coordination, etc. Large amount of data generated from victims, sensors and responders must be delivered and processed in a fast and reliable way, even when the normal communication infrastructure is degraded or destroyed. To this end, reconstructing the post-disaster network by deploying MDRU (Movable and Deployable Resource Unit) and relay unit at edge is a very promising solution. However, the optimal wireless access control in this heterogeneous hastily formed network is extremely challenging, due to the frequent varying environment and the lack of statistics information in advance in post-disaster scenarios. In this paper, we propose a learning based wireless access control approach for edgeaided disaster response network. More specifically, we model the wireless access control procedure as a discrete-time single agent Markov decision process, and solve the problem by exploiting deep reinforcement learning technique. By extensive simulation results, we show that the proposed mechanism significantly outperforms the baseline schemes in terms of delay and packet drop rate.

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Section: Related Workmentioning

confidence: 99%

Wireless Access Control in Edge-Aided Disaster Response: A Deep Reinforcement Learning-Based Approach

et al. 2021

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“…In [155], a low-complexity mechanism for relay scheduling in cooperative IoT networks using a stateless RL method -the multiarmed bandit (MAB) -is investigated. The authors utilized the MAB framework to learn relay scheduling using only the acknowledgments (and negative acknowledgments) of packet transmissions.…”

Section: B Reinforcement Learningmentioning

confidence: 99%

Relaying in the Internet of Things (IoT): A Survey

Uyoata¹,

Mwangama

Adeogun

2021

IEEE Access

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The deployment of relays between Internet of Things (IoT) end devices and gateways can improve link quality. In cellular-based IoT, relays have the potential to reduce base station overload. The energy expended in single-hop long-range communication can be reduced if relays listen to transmissions of end devices and forward these observations to gateways. However, incorporating relays into IoT networks faces some challenges. IoT end devices are designed primarily for uplink communication of small-sized observations toward the network; hence, opportunistically using end devices as relays needs a redesign of both the medium access control (MAC) layer protocol of such end devices and possible addition of new communication interfaces. Additionally, the wake-up time of IoT end devices needs to be synchronized with that of the relays. For cellular-based IoT, the possibility of using infrastructure relays exists, and noncellular IoT networks can leverage the presence of mobile devices for relaying, for example, in remote healthcare. However, the latter presents problems of incentivizing relay participation and managing the mobility of relays. Furthermore, although relays can increase the lifetime of IoT networks, deploying relays implies the need for additional batteries to power them. This can erode the energy efficiency gain that relays offer. Therefore, designing relay-assisted IoT networks that provide acceptable trade-offs is key, and this goes beyond adding an extra transmit RF chain to a relay-enabled IoT end device. There has been increasing research interest in IoT relaying, as demonstrated in the available literature. Works that consider these issues are surveyed in this paper to provide insight into the state of the art, provide design insights for network designers and motivate future research directions.

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“…Thus, AI-based algorithms can avoid the massive iterations and alleviate the computation overhead of conventional methods. Moreover, the RL and DRL techniques can efficiently address the problem of the huge size of solution space [171], [179]. Furthermore, the combinations of ML/DL models with heuristic algorithms or game theory can further enhance efficiency [79], [117], [134], [161], [180] IV.…”

Section: E Summarymentioning

confidence: 99%

AI based Service Management for 6G Green Communications

Mao,

Tang,

Yuichi

et al. 2021

Preprint

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Green communications have always been a target for the information industry to alleviate energy overhead and reduce fossil fuel usage. In current 5G and future 6G era, there is no doubt that the volume of network infrastructure and the number of connected terminals will keep exponentially increasing, which results in the surging energy cost. It becomes growing important and urgent to drive the development of green communications. However, 6G will inevitably have increasingly stringent and diversified requirements for Quality of Service (QoS), security, flexibility, and even intelligence, all of which challenge the improvement of energy efficiency. Moreover, the dynamic energy harvesting process, which will be adopted widely in 6G, further complicates the power control and network management. To address these challenges and reduce human intervene, Artificial Intelligence (AI) has been widely recognized and acknowledged as the only solution. Academia and industry have conducted extensive research to alleviate energy demand, improve energy efficiency, and manage energy harvesting in various communication scenarios. In this paper, we present the main considerations for green communications and survey the related research on AI-based green communications. We focus on how AI techniques are adopted to manage the network and improve energy harvesting toward the green era. We analyze how state-of-the-art Machine Learning (ML) and Deep Learning (DL) techniques can cooperate with conventional AI methods and mathematical models to reduce the algorithm complexity and optimize the accuracy rate to accelerate the applications in 6G. Finally, we discuss the existing problems and envision the challenges for these emerging techniques in 6G.

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Deep Reinforcement Learning-Based Access Control for Buffer-Aided Relaying Systems With Energy Harvesting

Cited by 8 publications

References 30 publications

Wireless Access Control in Edge-Aided Disaster Response: A Deep Reinforcement Learning-Based Approach

Wireless Access Control in Edge-Aided Disaster Response: A Deep Reinforcement Learning-Based Approach

Relaying in the Internet of Things (IoT): A Survey

AI based Service Management for 6G Green Communications

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